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To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)–diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM–DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mm2 up to 3 averages, 1.5 T: TR = 5.774 s, TE = 81 ms and 3 T: TR = 4.899 s, TE = 100 ms), T2-weighted, and T1-weighted imaging were used on the 1.5 T and 3 T MRI scanner, respectively. The IVIM–DKI signal was modeled using the traditional IVIM–DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM–DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5 T) and the traditional model (at 3 T) to characterize prostate lesions. IVIM–DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50–78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T. In comparison with the IVIM–DKI model at 3 T, IVIM–DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

OBJECTIVE. The objective of our study was to assess and compare the diagnostic accuracy of perfusion CT (PCT) and dual-energy CT (DECT) in differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC. MATERIALS AND METHODS. This retrospective study included 51 patients with 52 renal cell carcinomas (RCCs) (36 ccRCCs and 16 non-ccRCCs) who underwent both PCT and DECT before surgery or biopsy between January 2014 and December 2018. Three independent readers measured blood flow, blood volume (BV), and permeability using PCT and iodine concentration (IC) and iodine ratio using DECT. Interreader agreement was calculated using the intraclass correlation coefficient (ICC). Multivariable logistic regression analysis was performed to assess PCT and DECT models. Size-specific dose estimates of the two methods were compared. RESULTS. BV (ICC, 0.93) and iodine ratio (ICC, 0.85) were the most reproducible parameters. Both PCT and DECT were significant models (p 0.05). BV and iodine ratio were independent predictors of nonccRCC (p < 0.05). However, the mean size-specific dose estimate was 16 times lower with DECT than with PCT (p < 0.001). The AUC of iodine ratio was 0.95, and sensitivity, specificity, and accuracy with an iodine ratio cutoff of 63.72% was 0.90, 0.86, and 0.87, respectively. CONCLUSION. PCT and DECT had comparable and high diagnostic accuracy in differentiating RCC subtypes; however, because of the significantly lower radiation dose of DECT, iodine ratio may be used as the best independent predictor.

BackgroundClinical or biochemical markers that have good correlation with magnetic resonance proton density fat fraction (MR PDFF) can be used as simple tools for the screening for nonalcoholic fatty liver disease (NAFLD) and in determining the degree of fatty infiltration of the liver. The objective of this study was to determine the degree of relationship between MR PDFF and ultrasonography (USG) grades of fatty liver, and clinical and biochemical parameters of adolescents and to determine the sensitivity and specificity of USG for diagnosis of NAFLD.MethodsThis prospective study included 34 overweight adolescents (mean age, 12.1 ± 1.5 years; range, 10–15.1 years; 10 girls and 24 boys) who underwent both USG and magnetic resonance imaging (MRI). Correlation analysis was performed between MR fat fraction and USG grades of fatty liver, and clinical and biochemical parameters of fatty liver disease.ResultsMR fat fraction had a moderate positive correlation with serum alanine transaminase (ALT) and aspartate transaminase (AST) (ρ = 0.634, p ConclusionsSerum ALT and AST are potential biochemical markers to assess the degree of hepatic steatosis in NAFLD, which needs validation in further studies. USG can be used as a screening tool for NAFLD, but the diagnosis should be confirmed by estimating the MR fat fraction.

OBJECTIVE. The purpose of this study is to determine the degree of the relationship between perfusion CT (PCT) parameters and iodine concentration metrics derived from triple-bolus dual-energy CT (DECT) and to compare the radiation dose delivered. SUBJECTS AND METHODS. This single-center prospective study was conducted from October 2015 to September 2017. Twenty-three consenting adults (15 men and eight women; mean [± SD] age, 56 ± 13 years [range, 25-78 years]) with renal cell carcinomas underwent consecutive PCT and triple-bolus DECT examinations. Triple-bolus DECT consisted of synchronous corticomedullary, nephrographic, and delayed phase scans acquired using a dual-source DECT scanner. Two readers independently analyzed blood flow, blood volume, and permeability, as measured by PCT, and iodine density and iodine ratio, as measured by triple-bolus DECT. Size-specific dose estimates were calculated for both groups. RESULTS. Interreader agreement was good for permeability (intraclass correlation coefficient [ICC] =.812) and blood flow (ICC = 0.849) and excellent for blood volume (ICC = 0.956), iodine density (ICC = 0.961), and iodine ratio (ICC = 0.956). Very strong positive correlations were found between blood volume and iodine density (p < 0.001) and between blood volume and iodine ratio (p < 0.001). Strong positive correlations were found between blood flow and iodine density (p < 0.001) and between blood flow and iodine ratio (p < 0.001). The correlations between permeability and iodine density (p = 0.01) and between permeability and iodine ratio (p = 0.02) were moderate. The mean size-specific dose estimate of triple-bolus DECT was approximately 15 times lower than that of PCT (p < 0.001). CONCLUSION. Quantitative iodine metrics derived from triple-bolus DECT showed significant correlation with CT parameters in renal cell carcinoma, with a significantly lower radiation dose.

Objective: Early detection of peripheral neuropathy is extremely important as leprosy is one of the treatable causes of peripheral neuropathy. The study was undertaken to assess the role of diffusion tensor imaging (DTI) in ulnar neuropathy in leprosy patients. Methods: This was a case–control study including 38 patients (72 nerves) and 5 controls (10 nerves) done between January 2017 and June 2019. Skin biopsy proven cases of leprosy, having symptoms of ulnar neuropathy (proven on nerve conduction study) were included. MRI was performed on a 3 T MR system. Mean cross-sectional area, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of ulnar nerve at cubital tunnel were calculated. Additional ancillary findings and appearance of base sequences were evaluated. Results: Ulnar nerve showed thickening with altered T2W signal in all the affected nerves, having an average cross-sectional area of 0.26 cm2. Low FA with mean of 0.397 ± 0.19 and high ADC with mean of 1.28 ± 0.427 x 10 −3 mm2/s of ulnar nerve in retrocondylar groove was obtained. In the control group, mean cross-sectional area was 0.71cm2 with mean FA and ADC of 0.53 ± 0.088 and 1.03 ± 0.24 x 10 −3 mm2/s respectively. Statistically no significant difference was seen in diseased and control group. Cut-off to detect neuropathy for FA and ADC is 0.4835 and 1.1020 × 10 −3 mm2/s respectively. Conclusion: DTI though is challenging in peripheral nerves, however, is proving to be a powerful complementary tool for assessment of peripheral neuropathy. Our study validates its utility in infective neuropathies. Advances in knowledge: 1. DTI is a potential complementary tool for detection of peripheral neuropathies and can be incorporated in standard MR neurography protocol. 2. In leprosy-related ulnar neuropathy, altered signal intensity with thickening or abscess of the nerve is appreciated along with locoregional nodes and secondary denervation changes along with reduction of FA and rise in ADC value. 3. Best cut-offs obtained in our study for FA and ADC are 0.4835 and 1.1020 × 10 −3 mm2/s respectively.

Prostate cancer is the fourth most common cancer and population-based screening programmes are being increasingly adopted worldwide. Screening-positive patients undergo routine transrectal ultrasound (TRUS)-guided systematic biopsy, which is the current diagnostic standard for prostate cancer. However, systematic biopsies suffer from poor sensitivity, especially for the tumors of the anterior prostate and apex as well as in large volume glands. In the past decade, MRI-guided targeted biopsies have come up, which utilize the multiparametric capability of MRI to target lesions for sampling. MRI/TRUS fusion biopsies combine the advantages of MRI-targeting with that of real-time guidance made possible by TRUS. MRI-TRUS fusion biopsies are being increasingly used in men with high clinical suspicion of prostate cancer who have had prior negative systematic biopsies. A large number of fusion biopsy platforms are currently available commercially. Although the basic workflow is similar, there are differences in the operational software, biopsy routes offered, TRUS acquisition technique, type of correction applied at the time of fusion and in the probe tracking hardware. The article describes the current role and indications of MRI-TRUS fusion biopsy followed by a discussion on the workflow, patient preparation, biopsy procedure and complications.

Rationale and Objectives To compare dual energy CT (DECT) quantitative metrics and radiomics for differentiating benign and malignant pancreatic lesions on contrast enhanced abdomen CT. Materials and Methods Our study included 103 patients who underwent contrast-enhanced DECT for assessing focal pancreatic lesions at one of the two hospitals (Site A: age 68 ± 12 yrs; malignant = 41, benign = 18; Site B: age 46 ± 2 yrs; malignant = 23, benign = 21). All malignant lesions had histologic confirmation, and benign lesions were stable on follow up CT (>12 months) or had characteristic benign features on MRI. Arterial-phase, low- and high-kV DICOM images were processed with the DECT Tumor Analysis (DETA) to obtain DECT quantitative metrics such as HU, iodine and water content from a region of interest (ROI) over focal pancreatic lesions. Separately, we obtained DECT radiomics from the same ROI. Data were analyzed with multiple logistic regression and receiver operating characteristics to generate area under the curve (AUC) for best predictive variables. Results DECT quantitative metrics and radiomics had AUCs of 0.98-0.99 at site A and 0.89-0.94 at site B data for classifying benign and malignant pancreatic lesions. There was no significant difference in the AUCs and accuracies of DECT quantitative metrics and radiomics from lesion rims and volumes among patients at both sites (p > 0.05). Supervised learning-based model with data from the two sites demonstrated best AUCs of 0.94 (DECT radiomics) and 0.90 (DECT quantitative metrics) for characterizing pancreatic lesions as benign or malignant. Conclusion Compared to complex DECT radiomics, quantitative DECT information provide a simpler but accurate method of differentiating benign and malignant pancreatic lesions.

To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM-DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH).Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mmIVIM-DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50-78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T.In comparison with the IVIM-DKI model at 3 T, IVIM-DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

Background Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet. Objective This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion. Methods An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions’ diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set. Results The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively. Conclusions Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.

Transrectal ultrasound (TRUS)-guided systematic biopsy, the current gold standard for the detection of prostate cancer, suffers from low sensitivity for clinically significant cancer. The use of diagnostic multiparametric MRI has increased the relevance of targeted biopsy techniques such as MRI-TRUS fusion biopsy and direct (in-bore) MRI-guided biopsy, which have higher detection rate for clinically significant cancer. Although primarily used in patients who remain at high clinical suspicion for prostate cancer despite a negative systematic biopsy, with the increasing use of upfront diagnostic MRI, these biopsies are expected to replace routine systematic biopsies. This pictorial essay aims to enhance our understanding of the concepts of these biopsy techniques so that they can be performed safely and provide maximum diagnostic yield.